Digital Commons Network^™

Interoperability Model And Application Of Military Training System For Combination Of Virtuality And Reality, Jianxing Gong, Hai Hu, Haihui Ren, Ruixiang Wu

Journal of System Simulation

Abstract: With the development of AI technology, VR technology, and combat simulation technology, in order to achieve the practical training effect of "how to fight and how to train soldiers", virtual and real training has become a widely popular military training mode. It has become a trend to integrate digital systems, virtual equipment, semi-physical models, physical models, and other heterogeneous systems to carry out training in the same training environment. Therefore, it is necessary to study the interoperability model and application of training systems for the combination of virtuality and reality. This paper proposed the definition of interoperability between virtuality …

Research On Infrared And Visible Light Fusion Method Based On Resnet-50 And Laplacian Filtering, Xiao Wang, Xiangyang Li, Feng Liang, Zhili Zhang

Journal of System Simulation

Abstract: In order to solve the problem that existing infrared and visible light image fusion techniques often suffer from artifacts caused by insufficient contrast, spectral distortion, and high computational complexity, a fusion framework based on ResNet-50 and Laplacian filtering was proposed. ResNet-50 was used to extract shallow and deep features, followed by multi-scale feature fusion. Laplacian filtering was applied to optimize feature information, and an automatic discriminator was introduced to further improve the fusion effect. Simulation results show that, compared with comparison algorithms, the proposed method achieves an average increase of 2.71% and 2.16% in information entropy, 5.98% and …

Analysis Of Optimal Spectral Bands For Thermal Infrared Hyperspectral Image Reconstruction Driven By Physical Simulation Model, Yonghao Yang, Xiaoyu He

Journal of System Simulation

Abstract: To achieve accurate reconstruction of thermal infrared hyperspectral images under limited spectral bands, this paper proposed a reconstruction method based on physical modeling and simulation. Semi-global decomposition algorithm was adopted to invert the thermophysical properties of the scenario based on the physical model of thermal radiation, simulating and generating full-band hyperspectral data. An optimal spectral band selection strategy driven by a physical model was proposed, which integrated the sensitivity of temperature inversion and the separability of material spectra. Experiments were conducted on both simulated and measured datasets to evaluate the performance of material identification, temperature inversion, and spectral …

Research On Pac-Bayes-Based A2c Algorithm For Multi-Objective Reinforcement Learning, Xiang Liu, Qiankun Jin

Journal of System Simulation

Abstract: To address the theoretical challenges of exploration and exploitation trade-offs and uncertainty modeling in multi-objective reinforcement learning (MORL), this study developed a learning framework, MO-PAC, based on PAC-Bayes theory. By introducing a multi-objective stochastic Critic network and a dynamic preference mechanism, the framework extended the conventional A2C architecture, enabling adaptive and efficient approximation of complex Pareto fronts. Experimental results demonstrate that in multi-objective MuJoCo environments, MO-PAC outperforms baseline algorithms, achieving approximately 20% improvement in hypervolume and 60% increase in expected utility, while exhibiting superior convergence efficiency and robustness. It verifies both theoretical value and practical performance advantages in …

Advanced Ni-Co And Sn-Based Alloys Electrodeposition From Aqueous And Deep Eutectic Solvents, Narmin M. Hamadamin, Hassan H. Abdallah

Polytechnic Journal

Electrodeposition is a pivotal technique in materials science, enabling the fabrication of metal and alloy coatings with tailored properties for diverse industrial applications. Traditional aqueous electrolytes, while widely used, present limitations such as narrow electrochemical windows, hydrogen evolution, and environmental concerns. Deep eutectic solvents (DESs) have emerged as promising alternatives, offering broader electrochemical windows, reduced toxicity, and enhanced solubility for metal salts. This review comprehensively examines the principles and advancements in electrodeposition from both aqueous and DES media, focusing on the deposition of Ni-Co and Sn-based alloys, the role of additives, the electrodeposition of metal powders, and surface analysis techniques. …

Dual-Channel Supply Chain Network Equilibrium Model Under Retailers’ Risk Aversion, Hongchun Wang, Caifeng Lin, Xinyi He, Haiyue Yin

Journal of System Simulation

Abstract: To study the network equilibrium problem of dual-channel supply chains under the background of retailers' risk aversion, a dual-channel supply chain network equilibrium model including multiple competitive suppliers, manufacturers, retailers, and demand markets was established. The Mean-CVaR method was employed to quantify retailers' risk aversion characteristics, and variational inequalities were utilized to characterize the equilibrium conditions of decision-makers at each tier of the supply chain. The projection contraction algorithm was applied to solve the model and conduct numerical analysis, thereby revealing the impact of retailers' risk aversion behavior on equilibrium outcomes. The simulation results indicate that a higher …

Spatiotemporal Graph Convolution-Based Demand Forecasting And Simulation Analysis For Automotive Parts Supply Chain, Xiaobin Li, Bing Hu, Chao Yin, Bo Li, Jun Ma

Journal of System Simulation

Abstract: To address complex automotive after-sales parts supply network operations with insufficient demand forecasting accuracy, slow response, and low service efficiency, this study proposed a spatiotemporal graph convolution-based method for automotive parts supply chain demand forecasting. Sales network data of the automotive parts sales network was constructed as a heterogeneous graph, integrating node features like parts sales volume and value to build multi-dimensional node dependencies. A node update mechanism of the graph convolutional neural network was designed, combined with long short-term memory neural networks to capture temporal features, using spatiotemporal attention to integrate temporal and spatial features into updated nodes …

A Method Of Heuristic Human-Llm Collaborative Source Search, Yi Chen, Sihang Qiu, Zhengqiu Zhu, Yatai Ji, Yong Zhao, Rusheng Ju

Journal of System Simulation

Abstract: Traditional source search algorithms are prone to local optimization, and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention. In this study, we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models (MLLMs) to achieve visual-language conversion, combined chain-of-thought (CoT) reasoning to optimize decision-making, and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration. The effectiveness of the framework was verified by experiments. The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual …

Read Well, Read Right: Developing Thai Reading Skills Of Third-Grade Students Through Phonics-Based Instruction Combined With Reading Skill Exercises, Wirachai Longkaeo, Sitah Thayati

Journal of Education Studies

This pre-experimental research aimed to 1) examine the effects of phonics-based instruction combined with reading skill exercises on Thai reading skills of third-grade students, and 2) investigate the learning process and factors contributing to the success of this instructional approach. A one-group pretest-posttest was employed. The target group consisted of 15 third-grade students from a private school in Chiang Mai Province, selected through purposive sampling based on their Thai reading assessment scores, identifying students with beginning-level reading foundations. The intervention lasted 10 hours. Research instruments included 1) a Thai reading skill test, and 2) observation and student reflection form. Quantitative …

Coincidence Points And Common Fixed Points Of Cyclic Maps By Implicit Contractive Conditions, Abbas Karim Nahi, Salwa Salman Abed

Baghdad Science Journal

The study of common fixed points and coincidence points has occupied a large part of the priorities of researchers, especially in the metric space and its generalizations. This done by weakening the assumption of commutativity by using: compatible mappings, weakly compatible mappings, R-sub weakly commuting mapping, Cq-commuting maps, occasionally weakly compatible maps, etc. The notion of occasionally weakly compatible and occasionally weakly biased mappings reduces to weak compatibility due to the unique coincidence points of the involved maps. It was pointed out that some results did not lead to effective generalizations for occasionally weakly compatible maps and occasionally weakly biased …

Using Physics-Informed Neural Networks For Solving 2Nd-Order Volterra Integro-Differential Equation By Deepxde Library, Oday Ahmed Jasim, Abdulghafor M. Al-Rozbayani

Baghdad Science Journal

Many disciplines widely use deep learning (DL) as a key tool for investigating the behavior of various systems. Deep learning (DL) has recently been utilized to solve differential equations using physics-based input. Physics-informed neural networks (PINNs) are a novel DL model that excels at solving both inverse and forward non-linear PDE problems. PINNs may be trained as surrogate models for approximation solutions to the VIDE without label data by embedding the physical information outlined by PDEs in feedforward NNs. The objective of this study is to solve the second-order Volterra integral-differential equations (2nd-order VIDEs) for the first time using PINN …

The Dynamic Among The Producer, Consumer, And Predator In The Presence Of Fear, Hunting Cooperation, And Anti-Predator Behavior, Muslim Saad Jabbar, Raid Kamel Naji

Baghdad Science Journal

In general, the dynamic interactions that form the relationships between predators and prey within a food chain depend heavily on fear, hunting cooperation, and anti-predator defenses. These elements support species’ survival in their particular habitats, balance the ecosystem, and control the numbers of predators and prey. Therefore, a three-species food chain system consisting of producer-consumer-predator, which plays a crucial role in ecological dynamics, has been formulated mathematically. The objective is to investigate the influence of fear, hunting cooperation, and anti-predators on the dynamic behavior of the food chain system. Two types of functional responses have been utilized. The characteristics of …

Robust Emergency Dispatch Method Considering Dynamic Frequency Security And N-K Contingency, Tao Huang, Zhi Zhang, Yujie Ding, Yanbo Chen, Jing Wang, Wenqian Zhang

Journal of System Simulation

Abstract: To address the risk of system inertia loss and frequency instability caused by grid integration of high-proportioned new energy and unit failures, an N-k robust emergency dispatch method considering dynamic frequency security constraints was proposed. With the consideration of the frequency response characteristics of variable-speed pumped storage, a dynamic frequency response model incorporating variable-speed pumped storage was constructed, and the nadir frequency constraint was established through second-order cone transformation. Information entropy theory was employed to quantify the uncertainty of unit failures, and an uncertainty set considering N-k unit failures was developed. A twostage robust emergency dispatch model considering N-k …

Scheduling Method For Virtual Power Plants Based On Analysis And Forecasting Of Heterogeneous Load Characteristics, Runzhao Zhang, Yanbo Chen, Tao Huang, Haoxin Tian, Tuben Qiang, Zhi Zhang

Journal of System Simulation

Abstract: To improve the electricity supply-demand situation by rationally utilizing demand response resources, a two-layer optimal scheduling model for virtual power plants (VPPs) based on the analysis and forecasting of heterogeneous load characteristics was proposed. With the differences in response characteristics of multi-type loads considered, a demand response model for multi-type loads was constructed by using a customer baseline load (CBL) curve forecasting method that integrated dynamic scenario generation and K-means++ clustering. A two-layer optimal scheduling model for VPPs that incorporated load aggregators and demand response was established. In this model, the upper layer conducted optimal scheduling targeting maximizing the …

Vibration Control Of Offshore Wind Turbine Towers Based On Eddy Current Nonlinear Energy Sink, Xiangxing Yu, Yandong Zhao, Baolin Zhang

Journal of System Simulation

Abstract: To address the issue of tower vibrations induced by wind loads, which can damage the structure of wind turbines, a vibration control method for monopile offshore wind turbine towers based on an eddy current-nonlinear energy sink (EC-NES) was proposed. The dynamic model of monopile offshore wind turbines based on EC-NES was constructed according to the Euler-Lagrange equation, and based on the output response of FAST software, the unknown parameters of the model and the wind loads were identified in terms of parameters. The optimal parameters of EC-NES stiffness and damping were obtained using PSO. The eddy current damper …

Fault Diagnosis Method For Photovoltaic Systems Based On Multi-Strategy Fusion, Bin Li, Yuchuo Wang

Journal of System Simulation

Abstract: To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize …

Numerical Simulations Of Ship Liquid Tank Sloshing Based On Graph Neural Networks, Wenkang Zhang, Xiaofeng Sun, Yiping Zhong, Yong Yin

Journal of System Simulation

Abstract: To address the high consumption of computational resources in simulating ship liquid tank sloshing using computational fluid dynamics simulation methods, a data-driven numerical simulation model was proposed based on graph neural networks. An encoder-processor-decoder framework was employed in the proposed model. The encoder extracted features of fluid particles from the first five time steps. The processor learnt latent motion patterns of fluid and updated features, and the decoder predicted features of particles at subsequent time steps. The processor incorporated a self-attention mechanism to enable dynamic adjacency weight allocation and emphasize the influence of irregular tank wall regions. Training …

Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang

Journal of System Simulation

Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck …

Measurement Of Luminous Intensity Distribution For Film And Television Led Light Sources And Its Simulation Research In Game Engines, Jingyi Suo, Baihong Lu, Che Qu

Journal of System Simulation

Abstract: To address the issues of mismatched photometric characteristics between light sources in virtual environments and real-world lighting during film and television lighting design and lighting preview using game engines, a testing solution for measuring the luminous intensity distribution for film and television LED light sources was proposed, building upon existing luminaire light intensity distribution testing systems. Based on the obtained data, a light source calibration process was constructed in the UE5 to correctly simulate the photometric characteristics of light sources in the virtual environment. Simulation results have shown that the calibration process can accurately and efficiently reproduce the …

Research On Cooperative Interference Allocation Of Jamming Resources Based On Improved Genetic Algorithm, Zhixia Xu, Rui Wang, Nan Sun, Bing He, Xiaowei Shen, Xiaofei Zhu

Journal of System Simulation

Abstract: To address the cooperative interference allocation of jamming tasks, a cooperative interference allocation method of jamming resources was proposed based on the improved genetic algorithm. In search and tracking modes of the target radar, a threat level assessment was conducted by the technique for order preference by similarity to an ideal solution (TOPSIS) based on the entropy weight method. The factors affecting the jamming effectiveness of jammers were analyzed. A cooperative interference evaluation model of jamming effectiveness was established, and the allocation model of jamming resources was built with the total interference effectiveness of multiple jammers as the …

Jupiter Mass Binary Objects Show A Minimum Acceleration, Mike Mcculloch

School of Engineering, Computing and Mathematics

Forty-two Jupiter Mass Binary Objects (JuMBOs) have been discovered in the Trapezium Cluster: either brown dwarf stars or planets mutually orbiting in pairs. Here it is shown that, just as in galaxies and wide binaries, the mutual orbits of the objects in each of these twin systems deviate from the Newtonian and level off around a mutual acceleration of 2��2/Θ=2×10−10 m/s2 supporting the minimum acceleration predicted by Quantised Inertia (QI), a theory that attributes inertial mass to an interaction between information horizons and quantum fields and predicts galaxy rotation without the need for dark matter. QI further predicts that the …

Satellite-Mediated Quantum Clock Synchronization: Towards Precise Timing At A Global Scale, Sage B. Ducoing

LSU Doctoral Dissertations

Accurate timekeeping is essential for scientific and technological advancements, particularly in areas of communication, networking, navigation, and high precision measurements. While many methods of time resolution are already established using classical resources, they fail to combine high precision outcomes with large-scale implementations. Additionally, numerous quantum networking architectures using satellite-assisted methods have demonstrated quantum communication on scales exceeding ground-based methods. For these reasons, we propose the use of a time synchronization method by which pairs of highly time-correlated photons are exchanged between clocks on satellites and clocks on Earth, al- lowing users to reconstruct the time offsets between their clocks. This …

Allfromair™ Ires (Gen 3) – System Architecture, Process Physics & Compliance Verification, Vesa Olavi Lius

Defensive Publications Series

This disclosure describes the ALLFROMAIR™ IRES (Gen 3), a unified thermodynamic platform integrating heating, cooling, ventilation, and atmospheric water generation (AWG) into a single hardware unit. The system utilizes an ”Anticubic” U-channel geometry with airflow velocities <2.5 m/s to minimize fan power according to the Cube Law. Key innovations include a ”Three-Stage Rocket” thermodynamic process and a ”Turbo Effect” where regenerative pre-cooling reduces compressor temperature lift (\Delta T \approx 5K), enabling practical COPs >30.

The disclosure specifically details a pressure-modulated spray nozzle matrix capable of switching between ”Flash Evaporation” (<300 µm droplets) and ”Film Wetting” modes to maximize latent heat transfer on Heresite P-413/Hydrophilic coated exchangers. The system solves historical displacement ventilation stratification (”The Bahco Problem”) via a scavenger heat exchanger (HX4) coupled to a hydronic floor loop. An integrated ”Water Ring” harvests and purifies condensate to EU Directive 2020/2184 standards, enabling decentralized potable water production for residential, commercial, and tropical applications.

Keywords:

Integrated Regenerative Energy System (IRES), ALLFROMAIR, Anticubic U-Channel, Low Lift Heat Pump, Atmospheric Water Generator (AWG), Pressure Modulated Spray Matrix, Flash Evaporation, Thermodynamic Up-Gearing, Heresite P-413, Hydrophilic Topcoat, Radiant-Assisted Displacement Ventilation, Twin-Leg Air Separation, EU Directive 2020/2184, Three-Stage Rocket Process.

Deterministic Workflow Generation From Ai-Generated Prototypes, Wai Tai

Defensive Publications Series

Generative artificial intelligence (AI) tools can accelerate workflow prototyping but remain inherently unreliable — the same prompt can yield inconsistent, incomplete, or broken outputs. Proposed herein is an innovation that bridges the gap between AI prototyping and production-grade automation by capturing validated “golden outputs,” extracting them into reusable briefs ("golden briefs"), and regenerating workflows deterministically through template-aware automation. Layered validation, including schema compliance, simulation with golden inputs, and regression testing ensures that every workflow produced is correct, reproducible, and ready for production deployment.

Reduction Of Ground Vibration Dynamic Effects Induced From Mining Blasting On Nearby Structures: Case Study, Ayman M. Ismail, Sherif H. M. Hassnien

HBRC Journal

One of the primary challenges confronting the quarrying industry pertains to the ground vibrations resulting from the blasting activities in cement quarries, as these vibrations have the potential to inflict significant damage upon adjacent structures. Within the context of this scholarly article, an examination is conducted on a residential edifice situated in the 15th of May city in Cairo, Egypt, which has been impacted by the proximate mining blasting. The ground velocity at the foundation of this residential building is assessed, mainly from the peak particle’s velocity prospective. Subsequently, a finite element model (FEM) of the building is formulated and …

A Proposed Model For Assessing The Impact Of Sustainable Practices On The Affordability Of Affordable Housing In Egypt Using Ahp, Ayman M Zakaria Eraqi, Omnia Nagy Abd El-Hafez, Amany Nagy Abd El-Hafez

HBRC Journal

Amid rising inflation and a housing crisis in Egypt, this research develops a model to assess the affordability of sustainable housing for low- and middle-income groups by integrating environmental, social, and economic sustainability criteria. The research methodology used Analytic Hierarchy Process (AHP) to evaluate the relative weights of sustainability criteria in housing projects. Based on their impact on construction costs, these criteria were classified by experts into five main groups: Design Criteria (preliminary studies, initial cost, environmental planning), Neighborhood Criteria (location, design, management), Social and Cultural Criteria (privacy and safety), Natural Resource Sustainability (water, energy), Housing Unit Criteria (design and …

A Generalized Convolutional Autoencoder Framework For Seismic Waveform Compression, Emad B. Helal, Mostafa M. Ibrahim, Ali G. Hafez

Almaaqal Journal of Sustainability and Emerging Technology

Effective data compression methods have become crucial as the amounts of seismic data are growing exponentially. Due to extensive surveys and ongoing real-time monitoring, compression not only reduces storage requirements but also optimizes communication bandwidth between remote seismic stations and central processing hubs. In this paper, a generalized autoencoder-based compression framework is proposed.. Its primary innovation is a single, fixed network architecture that delivers robust performance across low, medium, and high compression ratios (CRs). Performance was compared with a dynamic pooling-based model and three wavelet-based methods (db4, sym8, coif3) across CRs from 2 to 100. Experimental results demonstrate that the …

A Review Of Machine Learning Methods In Energy Management System, Mays Sattar Al-Fatllah, Zaipatimah Ali, Aidil Azwin Zainul Abidin

Almaaqal Journal of Sustainability and Emerging Technology

Traditional energy sources are vital for energizing residences and industries. Nonetheless, they meet considerable obstacles, such as deteriorating infrastructure, ecological decline, resource exhaustion, price fluctuations, and a disparity between real-time demand and supply. Conversely, renewable energy sources offer cleaner and more sustainable energy sources. Renewable energy mitigates the pollution caused by carbon and greenhouse gas emissions in traditional power systems. Incorporating renewable energy into a power system needs an efficient management system to keep stability and improve power use. Modern energy management systems (EMS) face a significant challenge in controlling renewable and non-renewable energy sources. The challenges encompass intermittent and …

Multivariate Optimization Of Nonlinear Response Functions Using Central Composite Design, Hussein M. Hussein, Bassma H. Elwakil, Esraa Abdelhamid Moneer, Sara H. Akl, Yahya H. Shahin

Almaaqal Journal of Sustainability and Emerging Technology

This study addresses critical challenges in multivariate optimization by: (1) developing efficient strategies for optimizing nonlinear response functions in high-dimensional parameter spaces, (2) quantifying comparative performance of traditional versus statistical design methodologies, (3) establishing mathematical frameworks for parameter interactions, and (4) validating results through comprehensive statistical analysis. A dual-phase optimization framework combined One-Variable-At-a-Time (OVAT) screening with Central Composite Design-based Response Surface Methodology (CCD-RSM). Five parameters were evaluated using rotatable CCD (α= 2.378) with 50 design points. Statistical analysis included analysis of variance (ANOVA), regression modeling, interaction quantification, and cross-validation. CCD-RSM demonstrated superior performance with model predictability improving from …

Machine Learning Framework For Automatic Classification Of Nano-Seismic Signals Preceding Earthquakes, Ghada Ali, Ali G. Hafez, Sayed Hasaneen, Hamed Nofel, Nabeel Al-Atwan, Ahmed Mohamed

Almaaqal Journal of Sustainability and Emerging Technology

Nano-seismicity preceding earthquakes has many applications in fault prediction, in structure health monitoring, and in enhancing fault and source parameter determination. Not all earthquakes are preceded by these precursors; therefore, there is a need to identify P-wave arrivals that are preceded by these nano signals, which will eliminate errors in P-wave arrival timing. The current work introduces topologies capable of automatically categorizing the arrivals with nano-seismic precursors. The proposed methods are trained on datasets extracted from stations belonging to Egyptian National Seismic Network (ENSN). These records contain both P-wave arrivals preceded by such nano precursors and other records without these …